https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf
local repo version of data-transformation.pdf
28 August, 2020
Data manipulation
Important data manipulation libraries
| Task | Function | Package |
|---|---|---|
| Sorting | order() |
base |
arrange() |
dplyr | |
| Re-ordering factor levels | factor(,levels=) |
base |
reorder(,new.order=) |
gdata | |
| Re-labelling | factor(,lab=) |
base |
recode() |
dplyr | |
revalue(,replace=) |
plyr | |
| Re-naming columns | colnames() |
base |
rename(,replace=) |
dplyr | |
| Filtering/Subsetting | indexing | base |
subset(,subset=,select=) |
base | |
select(,...) |
dplyr |
Important data manipulation libraries
| Task | Function | Package |
|---|---|---|
| Transformations | transform(), within() |
base |
mutate() |
dplyr | |
| Adding columns | within() |
base |
mutate() |
dplyr | |
| Reshaping data | gather(), spread() |
tidyr |
melt(), cast() |
reshape2 | |
| Aggregating | tapply() |
base |
summarize() group_by() |
dplyr | |
cast() |
reshape2 | |
summaryBy() |
doBy | |
| Merging/joining | merge() |
base |
*_join() |
dplyr |
Data manipulation grammar
Verbs
arrange()- sorting dataselect()- subset columnsrename()- rename columnsfilter()- subset rowsslice()mutate()- adding columssummarise()- aggregate (group_by())count()- tally
Data manipulation grammar
Tidying verbs
gather()- melt to long formatspread()- cast to wide formatunite()- combine columnsseparate()- separate columns
Multi data.frame verbs
*_join()- merging data
Data manipulation grammar
Piping
%>%
data %>%
select(...) %>%
group_by(...) %>%
summarise(...)
Data files
load(file = "../data/manipulationDatasets.RData")
OR
load(url("http://www.flutterbys.com.au/stats/downloads/
data/manipulationDatasets.RData"))
data.1
| Between | Plot | Cond | Time | Temp | LAT | LONG |
|---|---|---|---|---|---|---|
| A1 | P1 | H | 1 | 15.74 | 17.26 | 146.2 |
| A1 | P1 | M | 2 | 23.84 | 14.07 | 144.9 |
| A1 | P1 | L | 3 | 13.64 | 20.75 | 144.7 |
| A1 | P2 | H | 4 | 37.95 | 18.41 | 142.1 |
| A1 | P2 | M | 1 | 25.3 | 18.47 | 144 |
| A1 | P2 | L | 2 | 13.8 | 20.39 | 145.8 |
| A2 | P3 | H | 3 | 26.87 | 20.14 | 147.7 |
| A2 | P3 | M | 4 | 29.38 | 19.69 | 144.8 |
| A2 | P3 | L | 1 | 27.76 | 20.34 | 145.8 |
| A2 | P4 | H | 2 | 18.95 | 20.06 | 144.9 |
| A2 | P4 | M | 3 | 37.12 | 18.65 | 142.2 |
| A2 | P4 | L | 4 | 25.9 | 14.52 | 144.2 |
Data manipulation packages
library(dplyr) library(tidyr) #OR better still library(tidyverse)
Data files
head(data.1)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359
#OR data.1 %>% head
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359
Data files
summary(data.1)
## Between Plot Cond Time Temp LAT LONG ## A1:6 P1:3 H:4 Min. :1.00 Min. :13.64 Min. :14.07 Min. :142.1 ## A2:6 P2:3 L:4 1st Qu.:1.75 1st Qu.:18.14 1st Qu.:18.12 1st Qu.:144.1 ## P3:3 M:4 Median :2.50 Median :25.60 Median :19.17 Median :144.8 ## P4:3 Mean :2.50 Mean :24.69 Mean :18.56 Mean :144.8 ## 3rd Qu.:3.25 3rd Qu.:28.16 3rd Qu.:20.19 3rd Qu.:145.8 ## Max. :4.00 Max. :37.95 Max. :20.75 Max. :147.7
Data files
summary(data.1)
## Between Plot Cond Time Temp LAT LONG ## A1:6 P1:3 H:4 Min. :1.00 Min. :13.64 Min. :14.07 Min. :142.1 ## A2:6 P2:3 L:4 1st Qu.:1.75 1st Qu.:18.14 1st Qu.:18.12 1st Qu.:144.1 ## P3:3 M:4 Median :2.50 Median :25.60 Median :19.17 Median :144.8 ## P4:3 Mean :2.50 Mean :24.69 Mean :18.56 Mean :144.8 ## 3rd Qu.:3.25 3rd Qu.:28.16 3rd Qu.:20.19 3rd Qu.:145.8 ## Max. :4.00 Max. :37.95 Max. :20.75 Max. :147.7
data.1 %>% summary
## Between Plot Cond Time Temp LAT LONG ## A1:6 P1:3 H:4 Min. :1.00 Min. :13.64 Min. :14.07 Min. :142.1 ## A2:6 P2:3 L:4 1st Qu.:1.75 1st Qu.:18.14 1st Qu.:18.12 1st Qu.:144.1 ## P3:3 M:4 Median :2.50 Median :25.60 Median :19.17 Median :144.8 ## P4:3 Mean :2.50 Mean :24.69 Mean :18.56 Mean :144.8 ## 3rd Qu.:3.25 3rd Qu.:28.16 3rd Qu.:20.19 3rd Qu.:145.8 ## Max. :4.00 Max. :37.95 Max. :20.75 Max. :147.7
Data files
str(data.1)
## 'data.frame': 12 obs. of 7 variables: ## $ Between: Factor w/ 2 levels "A1","A2": 1 1 1 1 1 1 2 2 2 2 ... ## $ Plot : Factor w/ 4 levels "P1","P2","P3",..: 1 1 1 2 2 2 3 3 3 4 ... ## $ Cond : Factor w/ 3 levels "H","L","M": 1 3 2 1 3 2 1 3 2 1 ... ## $ Time : int 1 2 3 4 1 2 3 4 1 2 ... ## $ Temp : num 15.7 23.8 13.6 38 25.3 ... ## $ LAT : num 17.3 14.1 20.7 18.4 18.5 ... ## $ LONG : num 146 145 145 142 144 ...
Dense summary
glimpse(data.1)
## Rows: 12 ## Columns: 7 ## $ Between <fct> A1, A1, A1, A1, A1, A1, A2, A2, A2, A2, A2, A2 ## $ Plot <fct> P1, P1, P1, P2, P2, P2, P3, P3, P3, P4, P4, P4 ## $ Cond <fct> H, M, L, H, M, L, H, M, L, H, M, L ## $ Time <int> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4 ## $ Temp <dbl> 15.73546, 23.83643, 13.64371, 37.95281, 25.29508, 13.79532, 26.87429, 2… ## $ LAT <dbl> 17.25752, 14.07060, 20.74986, 18.41013, 18.46762, 20.38767, 20.14244, 1… ## $ LONG <dbl> 146.2397, 144.8877, 144.6884, 142.0585, 144.0437, 145.8359, 147.7174, 1…
Sorting data
Sorting data (arrange)
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
Sorting by LAT
arrange(data.1, LAT)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 M 2 23.83643 14.07060 144.8877 ## 2 A2 P4 L 4 25.89843 14.52130 144.1700 ## 3 A1 P1 H 1 15.73546 17.25752 146.2397 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A2 P4 M 3 37.11781 18.64913 142.2459 ## 7 A2 P3 M 4 29.38325 19.68780 144.7944 ## 8 A2 P4 H 2 18.94612 20.06427 144.8924 ## 9 A2 P3 H 3 26.87429 20.14244 147.7174 ## 10 A2 P3 L 1 27.75781 20.33795 145.7753 ## 11 A1 P2 L 2 13.79532 20.38767 145.8359 ## 12 A1 P1 L 3 13.64371 20.74986 144.6884
Sorting data (arrange)
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
Sorting by LAT (descending order)
arrange(data.1, -LAT)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 L 3 13.64371 20.74986 144.6884 ## 2 A1 P2 L 2 13.79532 20.38767 145.8359 ## 3 A2 P3 L 1 27.75781 20.33795 145.7753 ## 4 A2 P3 H 3 26.87429 20.14244 147.7174 ## 5 A2 P4 H 2 18.94612 20.06427 144.8924 ## 6 A2 P3 M 4 29.38325 19.68780 144.7944 ## 7 A2 P4 M 3 37.11781 18.64913 142.2459 ## 8 A1 P2 M 1 25.29508 18.46762 144.0437 ## 9 A1 P2 H 4 37.95281 18.41013 142.0585 ## 10 A1 P1 H 1 15.73546 17.25752 146.2397 ## 11 A2 P4 L 4 25.89843 14.52130 144.1700 ## 12 A1 P1 M 2 23.83643 14.07060 144.8877
Sorting data (arrange)
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
Sorting by Cond and then TEMP
arrange(data.1, Cond,Temp)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A2 P4 H 2 18.94612 20.06427 144.8924 ## 3 A2 P3 H 3 26.87429 20.14244 147.7174 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P1 L 3 13.64371 20.74986 144.6884 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 ## 7 A2 P4 L 4 25.89843 14.52130 144.1700 ## 8 A2 P3 L 1 27.75781 20.33795 145.7753 ## 9 A1 P1 M 2 23.83643 14.07060 144.8877 ## 10 A1 P2 M 1 25.29508 18.46762 144.0437 ## 11 A2 P3 M 4 29.38325 19.68780 144.7944 ## 12 A2 P4 M 3 37.11781 18.64913 142.2459
Sorting data (arrange)
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
Sort by the sum of Temp and LAT
arrange(data.1,Temp+LAT)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P2 L 2 13.79532 20.38767 145.8359 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P1 M 2 23.83643 14.07060 144.8877 ## 5 A2 P4 H 2 18.94612 20.06427 144.8924 ## 6 A2 P4 L 4 25.89843 14.52130 144.1700 ## 7 A1 P2 M 1 25.29508 18.46762 144.0437 ## 8 A2 P3 H 3 26.87429 20.14244 147.7174 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 ## 10 A2 P3 M 4 29.38325 19.68780 144.7944 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 ## 12 A1 P2 H 4 37.95281 18.41013 142.0585
Your turn
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
- sort by Between and then Cond
Your turn
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
- sort by Between and then Cond
arrange(data.1,Between,Cond)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P2 H 4 37.95281 18.41013 142.0585 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 L 2 13.79532 20.38767 145.8359 ## 5 A1 P1 M 2 23.83643 14.07060 144.8877 ## 6 A1 P2 M 1 25.29508 18.46762 144.0437 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 ## 8 A2 P4 H 2 18.94612 20.06427 144.8924 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 ## 10 A2 P4 L 4 25.89843 14.52130 144.1700 ## 11 A2 P3 M 4 29.38325 19.68780 144.7944 ## 12 A2 P4 M 3 37.11781 18.64913 142.2459
Your turn
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
- sort by Condition and then the ratio of Temp to LAT
Your turn
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
- sort by Condition and then the ratio of Temp to LAT
arrange(data.1,Cond,Temp/LAT)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A2 P4 H 2 18.94612 20.06427 144.8924 ## 3 A2 P3 H 3 26.87429 20.14244 147.7174 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P1 L 3 13.64371 20.74986 144.6884 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 ## 7 A2 P3 L 1 27.75781 20.33795 145.7753 ## 8 A2 P4 L 4 25.89843 14.52130 144.1700 ## 9 A1 P2 M 1 25.29508 18.46762 144.0437 ## 10 A2 P3 M 4 29.38325 19.68780 144.7944 ## 11 A1 P1 M 2 23.83643 14.07060 144.8877 ## 12 A2 P4 M 3 37.11781 18.64913 142.2459
Manipulating factors
Manipulating factors
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
levels(data.1$Cond)
## [1] "H" "L" "M"
- re-levelling
- re-labelling
- technically these operations are performed on single variables (vectors)
Re-levelling (sorting) factors
data.3 <- data.1 levels(data.3$Cond)
## [1] "H" "L" "M"
data.3$Cond <- factor(data.3$Cond, levels=c("L","M","H"))
levels(data.3$Cond)
## [1] "L" "M" "H"
head(data.3)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359
Re-levelling (sorting) factors
data.3 <- data.1 levels(data.3$Cond)
## [1] "H" "L" "M"
data.3$Cond <- factor(data.3$Cond, labels=c("High","Low","Medium"))
levels(data.3$Cond)
## [1] "High" "Low" "Medium"
head(data.3)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 High 1 15.73546 17.25752 146.2397 ## 2 A1 P1 Medium 2 23.83643 14.07060 144.8877 ## 3 A1 P1 Low 3 13.64371 20.74986 144.6884 ## 4 A1 P2 High 4 37.95281 18.41013 142.0585 ## 5 A1 P2 Medium 1 25.29508 18.46762 144.0437 ## 6 A1 P2 Low 2 13.79532 20.38767 145.8359
Re-levelling (sorting) factors
data.3 <- data.1 levels(data.3$Cond)
## [1] "H" "L" "M"
data.3$Cond <- factor(data.3$Cond, levels=c('L','M','H'),
labels=c("Low","Medium","High"))
levels(data.3$Cond)
## [1] "Low" "Medium" "High"
head(data.3)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 High 1 15.73546 17.25752 146.2397 ## 2 A1 P1 Medium 2 23.83643 14.07060 144.8877 ## 3 A1 P1 Low 3 13.64371 20.74986 144.6884 ## 4 A1 P2 High 4 37.95281 18.41013 142.0585 ## 5 A1 P2 Medium 1 25.29508 18.46762 144.0437 ## 6 A1 P2 Low 2 13.79532 20.38767 145.8359
Re-labelling factors
data.3 <- data.1 %>%
mutate(Cond=recode(Cond,'L'='Low', 'M'='Medium'))
levels(data.3$Cond)
## [1] "H" "Low" "Medium"
data.3
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 Medium 2 23.83643 14.07060 144.8877 ## 3 A1 P1 Low 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 Medium 1 25.29508 18.46762 144.0437 ## 6 A1 P2 Low 2 13.79532 20.38767 145.8359 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 ## 8 A2 P3 Medium 4 29.38325 19.68780 144.7944 ## 9 A2 P3 Low 1 27.75781 20.33795 145.7753 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 ## 11 A2 P4 Medium 3 37.11781 18.64913 142.2459 ## 12 A2 P4 Low 4 25.89843 14.52130 144.1700
Re-levelling & labelling
data.3 <- data.1 %>%
mutate(Cond=recode_factor(Cond,'L'='Low', 'M'='Medium'))
levels(data.3$Cond)
## [1] "Low" "Medium" "H"
data.3
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 Medium 2 23.83643 14.07060 144.8877 ## 3 A1 P1 Low 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 Medium 1 25.29508 18.46762 144.0437 ## 6 A1 P2 Low 2 13.79532 20.38767 145.8359 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 ## 8 A2 P3 Medium 4 29.38325 19.68780 144.7944 ## 9 A2 P3 Low 1 27.75781 20.33795 145.7753 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 ## 11 A2 P4 Medium 3 37.11781 18.64913 142.2459 ## 12 A2 P4 Low 4 25.89843 14.52130 144.1700
Re-levelling & labelling
You might also want to check out the forcats package
Subset columns
Selecting columns (select)
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
select(data.1, Between,Plot,Cond,Time,Temp)
## Between Plot Cond Time Temp ## 1 A1 P1 H 1 15.73546 ## 2 A1 P1 M 2 23.83643 ## 3 A1 P1 L 3 13.64371 ## 4 A1 P2 H 4 37.95281 ## 5 A1 P2 M 1 25.29508 ## 6 A1 P2 L 2 13.79532 ## 7 A2 P3 H 3 26.87429 ## 8 A2 P3 M 4 29.38325 ## 9 A2 P3 L 1 27.75781 ## 10 A2 P4 H 2 18.94612 ## 11 A2 P4 M 3 37.11781 ## 12 A2 P4 L 4 25.89843
Selecting columns (select)
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
select(data.1, -LAT,-LONG)
## Between Plot Cond Time Temp ## 1 A1 P1 H 1 15.73546 ## 2 A1 P1 M 2 23.83643 ## 3 A1 P1 L 3 13.64371 ## 4 A1 P2 H 4 37.95281 ## 5 A1 P2 M 1 25.29508 ## 6 A1 P2 L 2 13.79532 ## 7 A2 P3 H 3 26.87429 ## 8 A2 P3 M 4 29.38325 ## 9 A2 P3 L 1 27.75781 ## 10 A2 P4 H 2 18.94612 ## 11 A2 P4 M 3 37.11781 ## 12 A2 P4 L 4 25.89843
Selecting columns (select)
helper functions
contains()ends_with()starts_with()matches()everything()- …
must evaluate to TRUE/FALSE
Selecting columns (select)
helper functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
select(data.1, contains('L'))
## Plot LAT LONG ## 1 P1 17.25752 146.2397 ## 2 P1 14.07060 144.8877 ## 3 P1 20.74986 144.6884 ## 4 P2 18.41013 142.0585 ## 5 P2 18.46762 144.0437 ## 6 P2 20.38767 145.8359 ## 7 P3 20.14244 147.7174 ## 8 P3 19.68780 144.7944 ## 9 P3 20.33795 145.7753 ## 10 P4 20.06427 144.8924 ## 11 P4 18.64913 142.2459 ## 12 P4 14.52130 144.1700
Selecting columns (select)
helper functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
select(data.1, starts_with('L'))
## LAT LONG ## 1 17.25752 146.2397 ## 2 14.07060 144.8877 ## 3 20.74986 144.6884 ## 4 18.41013 142.0585 ## 5 18.46762 144.0437 ## 6 20.38767 145.8359 ## 7 20.14244 147.7174 ## 8 19.68780 144.7944 ## 9 20.33795 145.7753 ## 10 20.06427 144.8924 ## 11 18.64913 142.2459 ## 12 14.52130 144.1700
Selecting columns (select)
helper functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
select(data.1, ends_with('t'))
## Plot LAT ## 1 P1 17.25752 ## 2 P1 14.07060 ## 3 P1 20.74986 ## 4 P2 18.41013 ## 5 P2 18.46762 ## 6 P2 20.38767 ## 7 P3 20.14244 ## 8 P3 19.68780 ## 9 P3 20.33795 ## 10 P4 20.06427 ## 11 P4 18.64913 ## 12 P4 14.52130
Selecting columns (select)
helper functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
select(data.1, matches('^T[a-z]m.'))
## Time Temp ## 1 1 15.73546 ## 2 2 23.83643 ## 3 3 13.64371 ## 4 4 37.95281 ## 5 1 25.29508 ## 6 2 13.79532 ## 7 3 26.87429 ## 8 4 29.38325 ## 9 1 27.75781 ## 10 2 18.94612 ## 11 3 37.11781 ## 12 4 25.89843
Regular expressions (regexp)
https://github.com/rstudio/cheatsheets/raw/master/regex.pdf
local repo version of regex.pdf
Selecting columns (select)
helper functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
select(data.1, Between:Temp)
## Between Plot Cond Time Temp ## 1 A1 P1 H 1 15.73546 ## 2 A1 P1 M 2 23.83643 ## 3 A1 P1 L 3 13.64371 ## 4 A1 P2 H 4 37.95281 ## 5 A1 P2 M 1 25.29508 ## 6 A1 P2 L 2 13.79532 ## 7 A2 P3 H 3 26.87429 ## 8 A2 P3 M 4 29.38325 ## 9 A2 P3 L 1 27.75781 ## 10 A2 P4 H 2 18.94612 ## 11 A2 P4 M 3 37.11781 ## 12 A2 P4 L 4 25.89843
Your turn
head(nasa)
## lat long month year cloudhigh cloudlow ## 1 36.20000 -113.8 1 1995 26.0 7.5 ## 2 33.70435 -113.8 1 1995 20.0 11.5 ## 3 31.20870 -113.8 1 1995 16.0 16.5 ## 4 28.71304 -113.8 1 1995 13.0 20.5 ## 5 26.21739 -113.8 1 1995 7.5 26.0 ## 6 23.72174 -113.8 1 1995 8.0 30.0 ## cloudmid ozone pressure surftemp temperature ## 1 34.5 304 835 272.7 272.1 ## 2 32.5 304 940 279.5 282.2 ## 3 26.0 298 960 284.7 285.2 ## 4 14.5 276 990 289.3 290.7 ## 5 10.5 274 1000 292.2 292.7 ## 6 9.5 264 1000 294.1 293.6
Select lat, long, and cloud.. columns
Your turn
head(nasa)
## lat long month year cloudhigh cloudlow cloudmid ozone pressure surftemp temperature ## 1 36.20000 -113.8 1 1995 26.0 7.5 34.5 304 835 272.7 272.1 ## 2 33.70435 -113.8 1 1995 20.0 11.5 32.5 304 940 279.5 282.2 ## 3 31.20870 -113.8 1 1995 16.0 16.5 26.0 298 960 284.7 285.2 ## 4 28.71304 -113.8 1 1995 13.0 20.5 14.5 276 990 289.3 290.7 ## 5 26.21739 -113.8 1 1995 7.5 26.0 10.5 274 1000 292.2 292.7 ## 6 23.72174 -113.8 1 1995 8.0 30.0 9.5 264 1000 294.1 293.6
head(select(nasa, lat, long, starts_with("cloud")))
## lat long cloudhigh cloudlow cloudmid ## 1 36.20000 -113.8 26.0 7.5 34.5 ## 2 33.70435 -113.8 20.0 11.5 32.5 ## 3 31.20870 -113.8 16.0 16.5 26.0 ## 4 28.71304 -113.8 13.0 20.5 14.5 ## 5 26.21739 -113.8 7.5 26.0 10.5 ## 6 23.72174 -113.8 8.0 30.0 9.5
Your turn
tikus[1:10,c(1:3,76:77)]
## Psammocora contigua Psammocora digitata Pocillopora damicornis time rep ## V1 0 0 79 81 1 ## V2 0 0 51 81 2 ## V3 0 0 42 81 3 ## V4 0 0 15 81 4 ## V5 0 0 9 81 5 ## V6 0 0 72 81 6 ## V7 0 0 0 81 7 ## V8 0 0 16 81 8 ## V9 0 0 0 81 9 ## V10 0 0 16 81 10
Select rep, time and only Species that DONT contain pora
Your turn
Select rep, time and only Species that DONT contain pora
dplyr::select(tikus, -contains('pora'))
## OR if we wanted to alter the order...
dplyr::select(tikus, rep, time, everything(),-contains('pora'))
Select awkward names
dplyr::select(tikus, `Pocillopora damicornis`)
## Pocillopora damicornis ## V1 79 ## V2 51 ## V3 42 ## V4 15 ## V5 9 ## V6 72 ## V7 0 ## V8 16 ## V9 0 ## V10 16 ## V11 0 ## V12 0 ## V13 0 ## V14 0 ## V15 0 ## V16 0 ## V17 0 ## V18 0 ## V19 0 ## V20 0 ## V21 0 ## V22 0 ## V23 0 ## V24 0 ## V25 0 ## V26 0 ## V27 0 ## V28 0 ## V29 0 ## V30 0 ## V31 0 ## V32 0 ## V33 0 ## V34 0 ## V35 0 ## V36 0 ## V37 0 ## V38 0 ## V39 0 ## V40 0 ## V41 18 ## V42 0 ## V43 0 ## V44 0 ## V45 0 ## V46 0 ## V47 0 ## V48 0 ## V49 0 ## V50 0 ## V51 0 ## V52 0 ## V53 0 ## V54 0 ## V55 0 ## V56 0 ## V57 10 ## V58 0 ## V59 30 ## V60 0
Selecting a single variable
pull(data.1, Temp)
## [1] 15.73546 23.83643 13.64371 37.95281 25.29508 13.79532 26.87429 29.38325 27.75781 ## [10] 18.94612 37.11781 25.89843
Re-naming columns (vectors)
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
rename(data.1, Condition=Cond, Temperature=Temp)
## Between Plot Condition Time Temperature LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700
Filtering
Filtering
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
filter(data.1, Cond=='H')
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P2 H 4 37.95281 18.41013 142.0585 ## 3 A2 P3 H 3 26.87429 20.14244 147.7174 ## 4 A2 P4 H 2 18.94612 20.06427 144.8924
filter(data.1, Cond %in% c('H','M'))
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P2 H 4 37.95281 18.41013 142.0585 ## 4 A1 P2 M 1 25.29508 18.46762 144.0437 ## 5 A2 P3 H 3 26.87429 20.14244 147.7174 ## 6 A2 P3 M 4 29.38325 19.68780 144.7944 ## 7 A2 P4 H 2 18.94612 20.06427 144.8924 ## 8 A2 P4 M 3 37.11781 18.64913 142.2459
Filtering
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
filter(data.1, Cond=='H' & Temp<25)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A2 P4 H 2 18.94612 20.06427 144.8924
filter(data.1, Cond=='H' | Temp<25)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 L 2 13.79532 20.38767 145.8359 ## 6 A2 P3 H 3 26.87429 20.14244 147.7174 ## 7 A2 P4 H 2 18.94612 20.06427 144.8924
Your turn
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
Keep only those rows with Temp less than 20 and LAT greater than 20 or LONG less than 145
Your turn
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
Keep only those rows with Temp less than 20 and LAT greater than 20, or LONG less than 145
filter(data.1, Temp<20 & (LAT>20 | LONG <145))
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 L 3 13.64371 20.74986 144.6884 ## 2 A1 P2 L 2 13.79532 20.38767 145.8359 ## 3 A2 P4 H 2 18.94612 20.06427 144.8924
Your turn
glimpse(nasa)
## Rows: 41,472 ## Columns: 11 ## $ lat <dbl> 36.200000, 33.704348, 31.208696, 28.713043, 26.217391, 23.721739, 2… ## $ long <dbl> -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -… ## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1… ## $ year <int> 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1… ## $ cloudhigh <dbl> 26.0, 20.0, 16.0, 13.0, 7.5, 8.0, 14.5, 19.5, 22.5, 21.0, 19.0, 16.… ## $ cloudlow <dbl> 7.5, 11.5, 16.5, 20.5, 26.0, 30.0, 29.5, 26.5, 27.5, 26.0, 28.5, 28… ## $ cloudmid <dbl> 34.5, 32.5, 26.0, 14.5, 10.5, 9.5, 11.0, 17.5, 18.5, 16.5, 12.5, 13… ## $ ozone <dbl> 304, 304, 298, 276, 274, 264, 258, 252, 250, 250, 248, 248, 250, 24… ## $ pressure <dbl> 835, 940, 960, 990, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,… ## $ surftemp <dbl> 272.7, 279.5, 284.7, 289.3, 292.2, 294.1, 295.0, 298.3, 300.1, 300.… ## $ temperature <dbl> 272.1, 282.2, 285.2, 290.7, 292.7, 293.6, 294.6, 296.9, 297.8, 298.…
Filter to the largest ozone value for the second month of the last year
Your turn
Filter to the largest ozone value for the second month of the last year
filter(nasa,year==max(year) & month==2) %>%
arrange(-ozone) %>% head(5)
filter(nasa,year==max(year) & month==2) %>%
arrange(-ozone) %>% slice(1:5)
##OR
filter(nasa,year==max(year) & month==2 ) %>%
top_n(5, ozone)
Your turn
Filter to all ozone values between 320 and 325 in the first month of the last year
glimpse(nasa)
## Rows: 41,472 ## Columns: 11 ## $ lat <dbl> 36.200000, 33.704348, 31.208696, 28.713043, 26.217391, 23.721739, 2… ## $ long <dbl> -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -113.8000, -… ## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1… ## $ year <int> 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1… ## $ cloudhigh <dbl> 26.0, 20.0, 16.0, 13.0, 7.5, 8.0, 14.5, 19.5, 22.5, 21.0, 19.0, 16.… ## $ cloudlow <dbl> 7.5, 11.5, 16.5, 20.5, 26.0, 30.0, 29.5, 26.5, 27.5, 26.0, 28.5, 28… ## $ cloudmid <dbl> 34.5, 32.5, 26.0, 14.5, 10.5, 9.5, 11.0, 17.5, 18.5, 16.5, 12.5, 13… ## $ ozone <dbl> 304, 304, 298, 276, 274, 264, 258, 252, 250, 250, 248, 248, 250, 24… ## $ pressure <dbl> 835, 940, 960, 990, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,… ## $ surftemp <dbl> 272.7, 279.5, 284.7, 289.3, 292.2, 294.1, 295.0, 298.3, 300.1, 300.… ## $ temperature <dbl> 272.1, 282.2, 285.2, 290.7, 292.7, 293.6, 294.6, 296.9, 297.8, 298.…
Your turn
Filter to all ozone values between 320 and 325 in the first month of the last year
filter(nasa,ozone > 320 & ozone<325, month==first(month),
year==last(year))
##OR
filter(nasa,between(ozone,320,325), month==first(month),
year==last(year))
Slicing
Filtering by row number
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
slice(data.1, 1:4)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585
slice(data.1, c(1:4,7))
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A2 P3 H 3 26.87429 20.14244 147.7174
Sampling
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
sample_n(data.1, 10, replace=TRUE)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 L 3 13.64371 20.74986 144.6884 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A2 P3 M 4 29.38325 19.68780 144.7944 ## 5 A1 P2 L 2 13.79532 20.38767 145.8359 ## 6 A2 P3 H 3 26.87429 20.14244 147.7174 ## 7 A2 P4 L 4 25.89843 14.52130 144.1700 ## 8 A1 P2 L 2 13.79532 20.38767 145.8359 ## 9 A2 P3 M 4 29.38325 19.68780 144.7944 ## 10 A2 P3 H 3 26.87429 20.14244 147.7174
Sampling
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
sample_frac(data.1, 0.5, replace=TRUE)
## Between Plot Cond Time Temp LAT LONG ## 1 A2 P4 M 3 37.11781 18.64913 142.2459 ## 2 A1 P1 H 1 15.73546 17.25752 146.2397 ## 3 A1 P2 H 4 37.95281 18.41013 142.0585 ## 4 A2 P3 M 4 29.38325 19.68780 144.7944 ## 5 A2 P3 L 1 27.75781 20.33795 145.7753 ## 6 A2 P3 L 1 27.75781 20.33795 145.7753
Effects of filtering
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
#examine the levels of the Cond factor levels(data.1$Cond)
## [1] "H" "L" "M"
Effects of filtering
#subset the dataset to just Cond H data.3<-filter(data.1,Plot=='P1') #examine subset data data.3
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884
#examine the levels of the Cond factor levels(data.3$Cond)
## [1] "H" "L" "M"
levels(data.3$Plot)
## [1] "P1" "P2" "P3" "P4"
levels(data.3$Between)
## [1] "A1" "A2"
Effects of filtering
Correction - all factors
#subset the dataset to just Cond H data.3<-filter(data.1,Plot=='P1') #drop the unused factor levels from all factors data.3<-droplevels(data.3) #examine the levels of each factor levels(data.3$Cond)
## [1] "H" "L" "M"
levels(data.3$Plot)
## [1] "P1"
levels(data.3$Between)
## [1] "A1"
Effects of filtering
Correction - single factor
#subset the dataset to just Cond H data.3<-filter(data.1,Plot=='P1') #drop the unused factor levels from Cond data.3$Plot<-factor(data.3$Plot) #examine the levels of each factor levels(data.3$Cond)
## [1] "H" "L" "M"
levels(data.3$Plot)
## [1] "P1"
levels(data.3$Between)
## [1] "A1" "A2"
Adding columns
Mutate
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, LL=LAT+LONG)
## Between Plot Cond Time Temp LAT LONG LL ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 163.4972 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 158.9583 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 165.4383 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 160.4686 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 162.5113 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 166.2236 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 167.8598 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 164.4822 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 166.1133 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 164.9567 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 160.8950 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 158.6913
Mutate
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, logTemp=log(Temp))
## Between Plot Cond Time Temp LAT LONG logTemp ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 2.755917 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 3.171215 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 2.613279 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 3.636343 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 3.230610 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 2.624329 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 3.291170 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 3.380425 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 3.323517 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 2.941599 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 3.614097 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 3.254182
Mutate
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, MeanTemp=mean(Temp), cTemp=Temp-MeanTemp) ## OR if just want the centered variable.. #mutate(data.1, cTemp=Temp-mean(Temp))
## Between Plot Cond Time Temp LAT LONG MeanTemp cTemp ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 24.68638 -8.9509150 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 24.68638 -0.8499436 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 24.68638 -11.0426630 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 24.68638 13.2664312 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 24.68638 0.6087009 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 24.68638 -10.8910607 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 24.68638 2.1879137 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 24.68638 4.6968702 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 24.68638 3.0714367 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 24.68638 -5.7402607 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 24.68638 12.4314348 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 24.68638 1.2120555
Mutate
Window functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, leadTemp=lead(Temp), lagTemp=lag(Temp))
## Between Plot Cond Time Temp LAT LONG leadTemp lagTemp ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 23.83643 NA ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 13.64371 15.73546 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 37.95281 23.83643 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 25.29508 13.64371 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 13.79532 37.95281 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 26.87429 25.29508 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 29.38325 13.79532 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 27.75781 26.87429 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 18.94612 29.38325 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 37.11781 27.75781 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 25.89843 18.94612 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 NA 37.11781
Mutate
Window functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, rankTime=min_rank(Time),denseRankTime=dense_rank(Time))
## Between Plot Cond Time Temp LAT LONG rankTime denseRankTime ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 1 1 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 4 2 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 7 3 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 10 4 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 1 1 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 4 2 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 7 3 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 10 4 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 1 1 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 4 2 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 7 3 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 10 4
Mutate
Window functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, rowTemp=row_number(Temp), rowTime=row_number(Time))
## Between Plot Cond Time Temp LAT LONG rowTemp rowTime ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 3 1 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 5 4 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 1 7 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 12 10 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 6 2 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 2 5 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 8 8 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 10 11 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 9 3 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 4 6 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 11 9 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 7 12
Mutate
Window functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, ntile(Temp,4))
## Between Plot Cond Time Temp LAT LONG ntile(Temp, 4) ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 1 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 2 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 1 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 4 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 2 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 1 ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 3 ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 4 ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 3 ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 2 ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 4 ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 3
Mutate
Window functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, between(Temp,20,30))
## Between Plot Cond Time Temp LAT LONG between(Temp, 20, 30) ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 FALSE ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 TRUE ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 FALSE ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 FALSE ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 TRUE ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 FALSE ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 TRUE ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 TRUE ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 TRUE ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 FALSE ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 FALSE ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 TRUE
Mutate
Window functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, fTemp=ifelse(Temp<20, 'Low',
ifelse(between(Temp,20,30), 'Medium', 'High')))
## OR
mutate(data.1, fTemp=case_when(Temp<20 ~ 'Low',
between(Temp, 20, 30) ~ 'Medium',
Temp>30 ~ 'High'))
## Between Plot Cond Time Temp LAT LONG fTemp ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 Low ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 Medium ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 Low ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 High ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 Medium ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 Low ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 Medium ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 Medium ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 Medium ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 Low ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 High ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 Medium ## Between Plot Cond Time Temp LAT LONG fTemp ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 Low ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 Medium ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 Low ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 High ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 Medium ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 Low ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 Medium ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 Medium ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 Medium ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 Low ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 High ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 Medium
Mutate
Window functions
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
mutate(data.1, fTemp=cut(Temp, breaks=c(0,20,30,100),
labels=c('Low','Medium','High')))
## Between Plot Cond Time Temp LAT LONG fTemp ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 Low ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 Medium ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 Low ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 High ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 Medium ## 6 A1 P2 L 2 13.79532 20.38767 145.8359 Low ## 7 A2 P3 H 3 26.87429 20.14244 147.7174 Medium ## 8 A2 P3 M 4 29.38325 19.68780 144.7944 Medium ## 9 A2 P3 L 1 27.75781 20.33795 145.7753 Medium ## 10 A2 P4 H 2 18.94612 20.06427 144.8924 Low ## 11 A2 P4 M 3 37.11781 18.64913 142.2459 High ## 12 A2 P4 L 4 25.89843 14.52130 144.1700 Medium
Summarising (aggregating) data
Summarise
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
summarise(data.1, MeanTemp=mean(Temp), VarTemp=var(Temp), N=n())
## MeanTemp VarTemp N ## 1 24.68638 65.72792 12
SE <- function(x) sd(x)/sqrt(length(x))
summarise(data.1, MeanTemp=mean(Temp), VarTemp=var(Temp),
SEM=SE(Temp))
## MeanTemp VarTemp SEM ## 1 24.68638 65.72792 2.340369
Summarise
Across versions
summarise(data.1, across(c(Temp,LAT), list(Mean=mean, Var=var)))
## Temp_Mean Temp_Var LAT_Mean LAT_Var ## 1 24.68638 65.72792 18.56219 5.048825
summarise(data.1, across(where(is.numeric), list(Mean=mean, Var=var)))
## Time_Mean Time_Var Temp_Mean Temp_Var LAT_Mean LAT_Var LONG_Mean LONG_Var ## 1 2.5 1.363636 24.68638 65.72792 18.56219 5.048825 144.7791 2.512696
summarize(data.1, across(where(is.numeric), mean),
across(where(is.factor), length)
)
## Time Temp LAT LONG Between Plot Cond ## 1 2.5 24.68638 18.56219 144.7791 12 12 12
Summarise
count(data.1, Cond)
## Cond n ## 1 H 4 ## 2 L 4 ## 3 M 4
count(data.1, Cond,between(Temp,20,30))
## Cond between(Temp, 20, 30) n ## 1 H FALSE 3 ## 2 H TRUE 1 ## 3 L FALSE 2 ## 4 L TRUE 2 ## 5 M FALSE 1 ## 6 M TRUE 3
Piping
Piping
head(data.1, 6)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359
data.1 %>% filter(Cond=='H') %>%
select(Cond, starts_with('t'))
## Cond Time Temp ## 1 H 1 15.73546 ## 2 H 4 37.95281 ## 3 H 3 26.87429 ## 4 H 2 18.94612
Grouping (=aggregating)
Grouping
head(data.1, 6)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359
data.1 %>% group_by(Between,Plot) %>%
summarise(Mean=mean(Temp))
## # A tibble: 4 x 3 ## # Groups: Between [2] ## Between Plot Mean ## <fct> <fct> <dbl> ## 1 A1 P1 17.7 ## 2 A1 P2 25.7 ## 3 A2 P3 28.0 ## 4 A2 P4 27.3
Grouping
head(data.1, 6)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877 ## 3 A1 P1 L 3 13.64371 20.74986 144.6884 ## 4 A1 P2 H 4 37.95281 18.41013 142.0585 ## 5 A1 P2 M 1 25.29508 18.46762 144.0437 ## 6 A1 P2 L 2 13.79532 20.38767 145.8359
data.1 %>% group_by(Between,Plot) %>%
summarise(Mean=mean(Temp), Var=var(Temp), N=n(),First=first(Temp))
## # A tibble: 4 x 6 ## # Groups: Between [2] ## Between Plot Mean Var N First ## <fct> <fct> <dbl> <dbl> <int> <dbl> ## 1 A1 P1 17.7 29.0 3 15.7 ## 2 A1 P2 25.7 146. 3 38.0 ## 3 A2 P3 28.0 1.62 3 26.9 ## 4 A2 P4 27.3 84.1 3 18.9
Grouping
mutate vs summarise
data.1 %>% group_by(Between,Plot) %>%
summarise(Mean=mean(Temp))
## # A tibble: 4 x 3 ## # Groups: Between [2] ## Between Plot Mean ## <fct> <fct> <dbl> ## 1 A1 P1 17.7 ## 2 A1 P2 25.7 ## 3 A2 P3 28.0 ## 4 A2 P4 27.3
data.1 %>% group_by(Between,Plot) %>%
mutate(Mean=mean(Temp))
## # A tibble: 12 x 8 ## # Groups: Between, Plot [4] ## Between Plot Cond Time Temp LAT LONG Mean ## <fct> <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> ## 1 A1 P1 H 1 15.7 17.3 146. 17.7 ## 2 A1 P1 M 2 23.8 14.1 145. 17.7 ## 3 A1 P1 L 3 13.6 20.7 145. 17.7 ## 4 A1 P2 H 4 38.0 18.4 142. 25.7 ## 5 A1 P2 M 1 25.3 18.5 144. 25.7 ## 6 A1 P2 L 2 13.8 20.4 146. 25.7 ## 7 A2 P3 H 3 26.9 20.1 148. 28.0 ## 8 A2 P3 M 4 29.4 19.7 145. 28.0 ## 9 A2 P3 L 1 27.8 20.3 146. 28.0 ## 10 A2 P4 H 2 18.9 20.1 145. 27.3 ## 11 A2 P4 M 3 37.1 18.6 142. 27.3 ## 12 A2 P4 L 4 25.9 14.5 144. 27.3
Grouping
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
data.1 %>% group_by(Between,Plot) %>%
mutate(Mean=mean(Temp), cTemp=Temp-Mean)
## # A tibble: 12 x 9 ## # Groups: Between, Plot [4] ## Between Plot Cond Time Temp LAT LONG Mean cTemp ## <fct> <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 A1 P1 H 1 15.7 17.3 146. 17.7 -2.00 ## 2 A1 P1 M 2 23.8 14.1 145. 17.7 6.10 ## 3 A1 P1 L 3 13.6 20.7 145. 17.7 -4.09 ## 4 A1 P2 H 4 38.0 18.4 142. 25.7 12.3 ## 5 A1 P2 M 1 25.3 18.5 144. 25.7 -0.386 ## 6 A1 P2 L 2 13.8 20.4 146. 25.7 -11.9 ## 7 A2 P3 H 3 26.9 20.1 148. 28.0 -1.13 ## 8 A2 P3 M 4 29.4 19.7 145. 28.0 1.38 ## 9 A2 P3 L 1 27.8 20.3 146. 28.0 -0.247 ## 10 A2 P4 H 2 18.9 20.1 145. 27.3 -8.37 ## 11 A2 P4 M 3 37.1 18.6 142. 27.3 9.80 ## 12 A2 P4 L 4 25.9 14.5 144. 27.3 -1.42
Grouping
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
data.1 %>% group_by(Between,Plot) %>% summarise(across(everything(), mean))
## # A tibble: 4 x 7 ## # Groups: Between [2] ## Between Plot Cond Time Temp LAT LONG ## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 A1 P1 NA 2 17.7 17.4 145. ## 2 A1 P2 NA 2.33 25.7 19.1 144. ## 3 A2 P3 NA 2.67 28.0 20.1 146. ## 4 A2 P4 NA 3 27.3 17.7 144.
Grouping
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
data.1 %>% select(-Cond,-Time) %>% group_by(Between,Plot) %>%
summarise_all(list(mean))
## # A tibble: 4 x 5 ## # Groups: Between [2] ## Between Plot Temp LAT LONG ## <fct> <fct> <dbl> <dbl> <dbl> ## 1 A1 P1 17.7 17.4 145. ## 2 A1 P2 25.7 19.1 144. ## 3 A2 P3 28.0 20.1 146. ## 4 A2 P4 27.3 17.7 144.
Grouping
head(data.1, 2)
## Between Plot Cond Time Temp LAT LONG ## 1 A1 P1 H 1 15.73546 17.25752 146.2397 ## 2 A1 P1 M 2 23.83643 14.07060 144.8877
data.1 %>% group_by(Between,Plot) %>%
summarise(across(c(Temp, LAT, LONG), list(Mean=mean, SE=SE)))
## # A tibble: 4 x 8 ## # Groups: Between [2] ## Between Plot Temp_Mean Temp_SE LAT_Mean LAT_SE LONG_Mean LONG_SE ## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 A1 P1 17.7 3.11 17.4 1.93 145. 0.487 ## 2 A1 P2 25.7 6.98 19.1 0.650 144. 1.09 ## 3 A2 P3 28.0 0.735 20.1 0.193 146. 0.859 ## 4 A2 P4 27.3 5.29 17.7 1.66 144. 0.790
Your turn
Calculate for each year, the mean abundance of Pocillopora damicornis
tikus[1:10,c(1:3,76:77)]
## Psammocora contigua Psammocora digitata Pocillopora damicornis time rep ## V1 0 0 79 81 1 ## V2 0 0 51 81 2 ## V3 0 0 42 81 3 ## V4 0 0 15 81 4 ## V5 0 0 9 81 5 ## V6 0 0 72 81 6 ## V7 0 0 0 81 7 ## V8 0 0 16 81 8 ## V9 0 0 0 81 9 ## V10 0 0 16 81 10
Your turn
Calculate for each year, the mean abundance of Pocillopora damicornis
tikus %>% group_by(time) %>%
summarise(MeanAbundance=mean(`Pocillopora damicornis`))
## # A tibble: 6 x 2 ## time MeanAbundance ## <fct> <dbl> ## 1 81 30 ## 2 83 0 ## 3 84 0 ## 4 85 0 ## 5 87 1.8 ## 6 88 4
Your turn
Calculate for each year, the number of samples as well as the mean and variance of ozone
nasa = as.data.frame(nasa) head(nasa)
## lat long month year cloudhigh cloudlow cloudmid ozone pressure surftemp temperature ## 1 36.20000 -113.8 1 1995 26.0 7.5 34.5 304 835 272.7 272.1 ## 2 33.70435 -113.8 1 1995 20.0 11.5 32.5 304 940 279.5 282.2 ## 3 31.20870 -113.8 1 1995 16.0 16.5 26.0 298 960 284.7 285.2 ## 4 28.71304 -113.8 1 1995 13.0 20.5 14.5 276 990 289.3 290.7 ## 5 26.21739 -113.8 1 1995 7.5 26.0 10.5 274 1000 292.2 292.7 ## 6 23.72174 -113.8 1 1995 8.0 30.0 9.5 264 1000 294.1 293.6
Your turn
Calculate for each year, the number of samples as well as the mean and variance of ozone
nasa %>% group_by(year) %>%
summarise(N=n(),Mean=mean(ozone), Var=var(ozone))
## # A tibble: 6 x 4 ## year N Mean Var ## <int> <int> <dbl> <dbl> ## 1 1995 6912 264. 258. ## 2 1996 6912 267. 326. ## 3 1997 6912 266. 327. ## 4 1998 6912 267. 507. ## 5 1999 6912 270. 368. ## 6 2000 6912 269. 353.
Reshaping data
Reshaping data frames
Wide data
| Between | Plot | Time.0 | Time.1 | Time.2 | |
|---|---|---|---|---|---|
| R1 | A1 | P1 | 8 | 14 | 14 |
| R2 | A1 | P2 | 10 | 12 | 11 |
| R3 | A2 | P3 | 7 | 11 | 8 |
| R4 | A2 | P4 | 11 | 9 | 2 |
Wide to long (melt)
data.w %>% gather(Time,Count,Time.0:Time.2) ## OR data.w %>% gather(Time,Count, -Between, -Plot)
## Between Plot Time Count ## 1 A1 P1 Time.0 8 ## 2 A1 P2 Time.0 10 ## 3 A2 P3 Time.0 7 ## 4 A2 P4 Time.0 11 ## 5 A1 P1 Time.1 14 ## 6 A1 P2 Time.1 12 ## 7 A2 P3 Time.1 11 ## 8 A2 P4 Time.1 9 ## 9 A1 P1 Time.2 14 ## 10 A1 P2 Time.2 11 ## 11 A2 P3 Time.2 8 ## 12 A2 P4 Time.2 2 ## Between Plot Time Count ## 1 A1 P1 Time.0 8 ## 2 A1 P2 Time.0 10 ## 3 A2 P3 Time.0 7 ## 4 A2 P4 Time.0 11 ## 5 A1 P1 Time.1 14 ## 6 A1 P2 Time.1 12 ## 7 A2 P3 Time.1 11 ## 8 A2 P4 Time.1 9 ## 9 A1 P1 Time.2 14 ## 10 A1 P2 Time.2 11 ## 11 A2 P3 Time.2 8 ## 12 A2 P4 Time.2 2
Reshaping data frames
Long data
| Resp1 | Resp2 | Between | Plot | Subplot | Within |
|---|---|---|---|---|---|
| 8 | 17 | A1 | P1 | S1 | B1 |
| 10 | 18 | A1 | P1 | S1 | B2 |
| 7 | 17 | A1 | P1 | S2 | B1 |
| 11 | 21 | A1 | P1 | S2 | B2 |
| 14 | 19 | A2 | P2 | S3 | B1 |
| 12 | 13 | A2 | P2 | S3 | B2 |
| 11 | 24 | A2 | P2 | S4 | B1 |
| 9 | 18 | A2 | P2 | S4 | B2 |
| 14 | 25 | A3 | P3 | S5 | B1 |
| 11 | 18 | A3 | P3 | S5 | B2 |
| 8 | 27 | A3 | P3 | S6 | B1 |
| 2 | 22 | A3 | P3 | S6 | B2 |
| 8 | 17 | A1 | P4 | S7 | B1 |
| 10 | 22 | A1 | P4 | S7 | B2 |
| 7 | 16 | A1 | P4 | S8 | B1 |
| 12 | 13 | A1 | P4 | S8 | B2 |
| 11 | 23 | A2 | P5 | S9 | B1 |
| 12 | 19 | A2 | P5 | S9 | B2 |
| 12 | 23 | A2 | P5 | S10 | B1 |
| 10 | 21 | A2 | P5 | S10 | B2 |
| 3 | 17 | A3 | P6 | S11 | B1 |
| 11 | 16 | A3 | P6 | S11 | B2 |
| 13 | 26 | A3 | P6 | S12 | B1 |
| 7 | 28 | A3 | P6 | S12 | B2 |
Reshaping data frames
head(data,2)
## Resp1 Resp2 Between Plot Subplot Within ## 1 8 17 A1 P1 S1 B1 ## 2 10 18 A1 P1 S1 B2
Widen (cast)
Widen Resp1 for repeated measures (Within)
data %>% select(-Resp2) %>% spread(Within,Resp1)
## Between Plot Subplot B1 B2 ## 1 A1 P1 S1 8 10 ## 2 A1 P1 S2 7 11 ## 3 A1 P4 S7 8 10 ## 4 A1 P4 S8 7 12 ## 5 A2 P2 S3 14 12 ## 6 A2 P2 S4 11 9 ## 7 A2 P5 S9 11 12 ## 8 A2 P5 S10 12 10 ## 9 A3 P3 S5 14 11 ## 10 A3 P3 S6 8 2 ## 11 A3 P6 S11 3 11 ## 12 A3 P6 S12 13 7
#reshape2:::cast(data,Between+Plot+Subplot~Within,value="Resp1")
Reshaping data frames
Widen Resp1 and Resp2 for repeated measures (Within)
head(data,2)
## Resp1 Resp2 Between Plot Subplot Within ## 1 8 17 A1 P1 S1 B1 ## 2 10 18 A1 P1 S1 B2
data %>% gather(Resp,Count,Resp1:Resp2)
## Between Plot Subplot Within Resp Count ## 1 A1 P1 S1 B1 Resp1 8 ## 2 A1 P1 S1 B2 Resp1 10 ## 3 A1 P1 S2 B1 Resp1 7 ## 4 A1 P1 S2 B2 Resp1 11 ## 5 A2 P2 S3 B1 Resp1 14 ## 6 A2 P2 S3 B2 Resp1 12 ## 7 A2 P2 S4 B1 Resp1 11 ## 8 A2 P2 S4 B2 Resp1 9 ## 9 A3 P3 S5 B1 Resp1 14 ## 10 A3 P3 S5 B2 Resp1 11 ## 11 A3 P3 S6 B1 Resp1 8 ## 12 A3 P3 S6 B2 Resp1 2 ## 13 A1 P4 S7 B1 Resp1 8 ## 14 A1 P4 S7 B2 Resp1 10 ## 15 A1 P4 S8 B1 Resp1 7 ## 16 A1 P4 S8 B2 Resp1 12 ## 17 A2 P5 S9 B1 Resp1 11 ## 18 A2 P5 S9 B2 Resp1 12 ## 19 A2 P5 S10 B1 Resp1 12 ## 20 A2 P5 S10 B2 Resp1 10 ## 21 A3 P6 S11 B1 Resp1 3 ## 22 A3 P6 S11 B2 Resp1 11 ## 23 A3 P6 S12 B1 Resp1 13 ## 24 A3 P6 S12 B2 Resp1 7 ## 25 A1 P1 S1 B1 Resp2 17 ## 26 A1 P1 S1 B2 Resp2 18 ## 27 A1 P1 S2 B1 Resp2 17 ## 28 A1 P1 S2 B2 Resp2 21 ## 29 A2 P2 S3 B1 Resp2 19 ## 30 A2 P2 S3 B2 Resp2 13 ## 31 A2 P2 S4 B1 Resp2 24 ## 32 A2 P2 S4 B2 Resp2 18 ## 33 A3 P3 S5 B1 Resp2 25 ## 34 A3 P3 S5 B2 Resp2 18 ## 35 A3 P3 S6 B1 Resp2 27 ## 36 A3 P3 S6 B2 Resp2 22 ## 37 A1 P4 S7 B1 Resp2 17 ## 38 A1 P4 S7 B2 Resp2 22 ## 39 A1 P4 S8 B1 Resp2 16 ## 40 A1 P4 S8 B2 Resp2 13 ## 41 A2 P5 S9 B1 Resp2 23 ## 42 A2 P5 S9 B2 Resp2 19 ## 43 A2 P5 S10 B1 Resp2 23 ## 44 A2 P5 S10 B2 Resp2 21 ## 45 A3 P6 S11 B1 Resp2 17 ## 46 A3 P6 S11 B2 Resp2 16 ## 47 A3 P6 S12 B1 Resp2 26 ## 48 A3 P6 S12 B2 Resp2 28
Reshaping data frames
Widen Resp1 and Resp2 for repeated measures (Within)
head(data,2)
## Resp1 Resp2 Between Plot Subplot Within ## 1 8 17 A1 P1 S1 B1 ## 2 10 18 A1 P1 S1 B2
data %>% gather(Resp,Count,Resp1:Resp2) %>% unite(WR,Within,Resp)
## Between Plot Subplot WR Count ## 1 A1 P1 S1 B1_Resp1 8 ## 2 A1 P1 S1 B2_Resp1 10 ## 3 A1 P1 S2 B1_Resp1 7 ## 4 A1 P1 S2 B2_Resp1 11 ## 5 A2 P2 S3 B1_Resp1 14 ## 6 A2 P2 S3 B2_Resp1 12 ## 7 A2 P2 S4 B1_Resp1 11 ## 8 A2 P2 S4 B2_Resp1 9 ## 9 A3 P3 S5 B1_Resp1 14 ## 10 A3 P3 S5 B2_Resp1 11 ## 11 A3 P3 S6 B1_Resp1 8 ## 12 A3 P3 S6 B2_Resp1 2 ## 13 A1 P4 S7 B1_Resp1 8 ## 14 A1 P4 S7 B2_Resp1 10 ## 15 A1 P4 S8 B1_Resp1 7 ## 16 A1 P4 S8 B2_Resp1 12 ## 17 A2 P5 S9 B1_Resp1 11 ## 18 A2 P5 S9 B2_Resp1 12 ## 19 A2 P5 S10 B1_Resp1 12 ## 20 A2 P5 S10 B2_Resp1 10 ## 21 A3 P6 S11 B1_Resp1 3 ## 22 A3 P6 S11 B2_Resp1 11 ## 23 A3 P6 S12 B1_Resp1 13 ## 24 A3 P6 S12 B2_Resp1 7 ## 25 A1 P1 S1 B1_Resp2 17 ## 26 A1 P1 S1 B2_Resp2 18 ## 27 A1 P1 S2 B1_Resp2 17 ## 28 A1 P1 S2 B2_Resp2 21 ## 29 A2 P2 S3 B1_Resp2 19 ## 30 A2 P2 S3 B2_Resp2 13 ## 31 A2 P2 S4 B1_Resp2 24 ## 32 A2 P2 S4 B2_Resp2 18 ## 33 A3 P3 S5 B1_Resp2 25 ## 34 A3 P3 S5 B2_Resp2 18 ## 35 A3 P3 S6 B1_Resp2 27 ## 36 A3 P3 S6 B2_Resp2 22 ## 37 A1 P4 S7 B1_Resp2 17 ## 38 A1 P4 S7 B2_Resp2 22 ## 39 A1 P4 S8 B1_Resp2 16 ## 40 A1 P4 S8 B2_Resp2 13 ## 41 A2 P5 S9 B1_Resp2 23 ## 42 A2 P5 S9 B2_Resp2 19 ## 43 A2 P5 S10 B1_Resp2 23 ## 44 A2 P5 S10 B2_Resp2 21 ## 45 A3 P6 S11 B1_Resp2 17 ## 46 A3 P6 S11 B2_Resp2 16 ## 47 A3 P6 S12 B1_Resp2 26 ## 48 A3 P6 S12 B2_Resp2 28
Reshaping data frames
Widen Resp1 and Resp2 for repeated measures (Within)
head(data,2)
## Resp1 Resp2 Between Plot Subplot Within ## 1 8 17 A1 P1 S1 B1 ## 2 10 18 A1 P1 S1 B2
data %>% gather(Resp,Count,Resp1:Resp2) %>% unite(WR,Within,Resp) %>%
spread(WR,Count)
## Between Plot Subplot B1_Resp1 B1_Resp2 B2_Resp1 B2_Resp2 ## 1 A1 P1 S1 8 17 10 18 ## 2 A1 P1 S2 7 17 11 21 ## 3 A1 P4 S7 8 17 10 22 ## 4 A1 P4 S8 7 16 12 13 ## 5 A2 P2 S3 14 19 12 13 ## 6 A2 P2 S4 11 24 9 18 ## 7 A2 P5 S9 11 23 12 19 ## 8 A2 P5 S10 12 23 10 21 ## 9 A3 P3 S5 14 25 11 18 ## 10 A3 P3 S6 8 27 2 22 ## 11 A3 P6 S11 3 17 11 16 ## 12 A3 P6 S12 13 26 7 28
Combining data
Merging data frames
Bio data (missing Subplot 3)
| Resp1 | Resp2 | Between | Plot | Subplot | |
|---|---|---|---|---|---|
| 1 | 8 | 18 | A1 | P1 | S1 |
| 2 | 10 | 21 | A1 | P1 | S2 |
| 4 | 11 | 23 | A1 | P2 | S4 |
| 5 | 14 | 22 | A2 | P3 | S5 |
| 6 | 12 | 24 | A2 | P3 | S6 |
| 7 | 11 | 23 | A2 | P4 | S7 |
| 8 | 9 | 20 | A2 | P4 | S8 |
| 9 | 14 | 11 | A3 | P5 | S9 |
| 10 | 11 | 22 | A3 | P5 | S10 |
| 11 | 8 | 24 | A3 | P6 | S11 |
| 12 | 2 | 16 | A3 | P6 | S12 |
Physio-chemical data (missing S7)
| Chem1 | Chem2 | Between | Plot | Subplot | |
|---|---|---|---|---|---|
| 1 | 1.453 | 0.8858 | A1 | P1 | S1 |
| 2 | 3.266 | 0.18 | A1 | P1 | S2 |
| 3 | 1.179 | 5.078 | A1 | P2 | S3 |
| 4 | 13.4 | 1.576 | A1 | P2 | S4 |
| 5 | 3.779 | 1.622 | A2 | P3 | S5 |
| 6 | 1.197 | 4.237 | A2 | P3 | S6 |
| 8 | 5.688 | 2.986 | A2 | P4 | S8 |
| 9 | 4.835 | 4.133 | A3 | P5 | S9 |
| 10 | 2.003 | 3.604 | A3 | P5 | S10 |
| 11 | 12.33 | 1.776 | A3 | P6 | S11 |
| 12 | 4.014 | 0.2255 | A3 | P6 | S12 |
Merging data frames
Merge bio and chem data (only keep full matches - an inner join)
inner_join(data.bio, data.chem)
## Resp1 Resp2 Between Plot Subplot Chem1 Chem2 ## 1 8 18 A1 P1 S1 1.452878 0.8858208 ## 2 10 21 A1 P1 S2 3.266253 0.1800177 ## 3 11 23 A1 P2 S4 13.400350 1.5762780 ## 4 14 22 A2 P3 S5 3.779183 1.6222430 ## 5 12 24 A2 P3 S6 1.196657 4.2369184 ## 6 9 20 A2 P4 S8 5.687807 2.9859003 ## 7 14 11 A3 P5 S9 4.834518 4.1328919 ## 8 11 22 A3 P5 S10 2.002931 3.6043314 ## 9 8 24 A3 P6 S11 12.326867 1.7763576 ## 10 2 16 A3 P6 S12 4.014221 0.2255188
- S3 and S7 absent
Merging data frames
Merge bio and chem data (keep all data - outer join)
full_join(data.bio,data.chem)
## Resp1 Resp2 Between Plot Subplot Chem1 Chem2 ## 1 8 18 A1 P1 S1 1.452878 0.8858208 ## 2 10 21 A1 P1 S2 3.266253 0.1800177 ## 3 11 23 A1 P2 S4 13.400350 1.5762780 ## 4 14 22 A2 P3 S5 3.779183 1.6222430 ## 5 12 24 A2 P3 S6 1.196657 4.2369184 ## 6 11 23 A2 P4 S7 NA NA ## 7 9 20 A2 P4 S8 5.687807 2.9859003 ## 8 14 11 A3 P5 S9 4.834518 4.1328919 ## 9 11 22 A3 P5 S10 2.002931 3.6043314 ## 10 8 24 A3 P6 S11 12.326867 1.7763576 ## 11 2 16 A3 P6 S12 4.014221 0.2255188 ## 12 NA NA A1 P2 S3 1.178652 5.0780682
- note the order of Subplot
Merging data frames
Merge bio and chem data (only keep full BIO matches - left join)
left_join(data.bio, data.chem)
## Resp1 Resp2 Between Plot Subplot Chem1 Chem2 ## 1 8 18 A1 P1 S1 1.452878 0.8858208 ## 2 10 21 A1 P1 S2 3.266253 0.1800177 ## 3 11 23 A1 P2 S4 13.400350 1.5762780 ## 4 14 22 A2 P3 S5 3.779183 1.6222430 ## 5 12 24 A2 P3 S6 1.196657 4.2369184 ## 6 11 23 A2 P4 S7 NA NA ## 7 9 20 A2 P4 S8 5.687807 2.9859003 ## 8 14 11 A3 P5 S9 4.834518 4.1328919 ## 9 11 22 A3 P5 S10 2.002931 3.6043314 ## 10 8 24 A3 P6 S11 12.326867 1.7763576 ## 11 2 16 A3 P6 S12 4.014221 0.2255188
Merging data frames
Merge bio and chem data (only keep full CHEM matches - right join)
right_join(data.bio, data.chem)
## Resp1 Resp2 Between Plot Subplot Chem1 Chem2 ## 1 8 18 A1 P1 S1 1.452878 0.8858208 ## 2 10 21 A1 P1 S2 3.266253 0.1800177 ## 3 11 23 A1 P2 S4 13.400350 1.5762780 ## 4 14 22 A2 P3 S5 3.779183 1.6222430 ## 5 12 24 A2 P3 S6 1.196657 4.2369184 ## 6 9 20 A2 P4 S8 5.687807 2.9859003 ## 7 14 11 A3 P5 S9 4.834518 4.1328919 ## 8 11 22 A3 P5 S10 2.002931 3.6043314 ## 9 8 24 A3 P6 S11 12.326867 1.7763576 ## 10 2 16 A3 P6 S12 4.014221 0.2255188 ## 11 NA NA A1 P2 S3 1.178652 5.0780682
VLOOKUP
VLOOKUP
Biological data set (data.bio)
## Resp1 Resp2 Between Plot Subplot ## 1 8 18 A1 P1 S1 ## 2 10 21 A1 P1 S2 ## 4 11 23 A1 P2 S4 ## 5 14 22 A2 P3 S5 ## 6 12 24 A2 P3 S6 ## 7 11 23 A2 P4 S7 ## 8 9 20 A2 P4 S8 ## 9 14 11 A3 P5 S9 ## 10 11 22 A3 P5 S10 ## 11 8 24 A3 P6 S11 ## 12 2 16 A3 P6 S12
Geographical data set (lookup table) (data.geo)
## Plot LAT LONG ## 1 P1 17.9605 145.4326 ## 2 P2 17.5210 146.1983 ## 3 P3 17.0011 146.3839 ## 4 P4 18.2350 146.7934 ## 5 P5 18.9840 146.0345 ## 6 P6 20.1154 146.4672
VLOOKUP
Incorporate (merge) the lat/longs into the bio data
left_join(data.bio,data.geo,by=c("Plot"))
## Resp1 Resp2 Between Plot Subplot LAT LONG ## 1 8 18 A1 P1 S1 17.9605 145.4326 ## 2 10 21 A1 P1 S2 17.9605 145.4326 ## 3 11 23 A1 P2 S4 17.5210 146.1983 ## 4 14 22 A2 P3 S5 17.0011 146.3839 ## 5 12 24 A2 P3 S6 17.0011 146.3839 ## 6 11 23 A2 P4 S7 18.2350 146.7934 ## 7 9 20 A2 P4 S8 18.2350 146.7934 ## 8 14 11 A3 P5 S9 18.9840 146.0345 ## 9 11 22 A3 P5 S10 18.9840 146.0345 ## 10 8 24 A3 P6 S11 20.1154 146.4672 ## 11 2 16 A3 P6 S12 20.1154 146.4672
Applied examples
Tikus Island coral data
## Psammocora contigua Psammocora digitata time rep ## V1 0 0 81 1 ## V2 0 0 81 2 ## V3 0 0 81 3 ## V4 0 0 81 4 ## V5 0 0 81 5 ## V6 0 0 81 6 ## V7 0 0 81 7 ## V8 0 0 81 8 ## V9 0 0 81 9 ## V10 0 0 81 10
## Rows: 60 ## Columns: 77 ## $ `Psammocora contigua` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Psammocora digitata` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Pocillopora damicornis` <int> 79, 51, 42, 15, 9, 72, 0, 16, 0, 1… ## $ `Pocillopora verrucosa` <int> 32, 21, 35, 0, 0, 0, 41, 25, 38, 0… ## $ `Stylopora pistillata` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Acropora bruegemanni` <int> 0, 44, 0, 11, 9, 10, 0, 0, 0, 37, … ## $ `Acropora robusta` <int> 0, 35, 40, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ `Acropora grandis` <int> 0, 0, 0, 0, 0, 0, 60, 0, 0, 0, 0, … ## $ `Acropora intermedia` <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Acropora formosa` <int> 75, 0, 15, 0, 125, 0, 0, 0, 10, 0,… ## $ `Acropora splendida` <int> 0, 22, 0, 31, 0, 9, 16, 0, 0, 20, … ## $ `Acropera aspera` <int> 17, 18, 9, 8, 23, 0, 17, 13, 16, 1… ## $ `Acropora hyacinthus` <int> 141, 34, 55, 54, 0, 0, 0, 0, 0, 0,… ## $ `Acropora palifera` <int> 32, 0, 44, 0, 17, 0, 0, 0, 0, 0, 0… ## $ `Acropora cytherea` <int> 108, 33, 14, 122, 0, 0, 0, 8, 0, 0… ## $ `Acropora tenuis` <int> 0, 25, 0, 0, 0, 22, 28, 0, 0, 0, 0… ## $ `Acropora pulchra` <int> 0, 0, 15, 52, 62, 33, 0, 0, 24, 0,… ## $ `Acropora nasuta` <int> 43, 21, 19, 0, 0, 0, 10, 0, 0, 0, … ## $ `Acropora humilis` <int> 31, 25, 0, 19, 0, 0, 0, 0, 0, 0, 0… ## $ `Acropora diversa` <int> 22, 19, 20, 13, 23, 14, 0, 12, 12,… ## $ `Acropora digitifera` <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Acropora divaricata` <int> 0, 32, 55, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ `Acropora subglabra` <int> 51, 0, 0, 44, 15, 0, 0, 25, 0, 0, … ## $ `Acropora cerealis` <int> 0, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Acropora valida` <int> 0, 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, … ## $ `Acropora acuminata` <int> 20, 0, 71, 0, 15, 0, 25, 25, 0, 0,… ## $ `Acropora elsevi` <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Acropora millepora` <int> 17, 14, 0, 20, 0, 0, 0, 0, 0, 0, 0… ## $ `Montipora monasteriata` <int> 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Montipora tuberculosa` <int> 0, 15, 15, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ `Montipora hispida` <int> 0, 0, 0, 32, 40, 24, 0, 0, 0, 0, 0… ## $ `Montipora digitata` <int> 0, 0, 0, 0, 0, 77, 84, 53, 71, 351… ## $ `Montipora foliosa` <int> 0, 0, 0, 0, 50, 71, 62, 81, 24, 0,… ## $ `Montipora verrucosa` <int> 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Fungia fungites` <int> 0, 0, 18, 17, 0, 0, 0, 0, 0, 0, 0,… ## $ `Fungia paumotensis` <int> 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 12,… ## $ `Fungia concina` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1… ## $ `Fungia scutaria` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Halomitra limax` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Pavona varians` <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30,… ## $ `Pavona venosa` <int> 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Pavona cactus` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Coeloseris mayeri` <int> 20, 0, 15, 0, 9, 19, 0, 0, 25, 0, … ## $ `Galaxea fascicularis` <int> 51, 27, 31, 24, 0, 13, 0, 0, 0, 0,… ## $ `Symphyllia radians` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Lobophyllia corymbosa` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1… ## $ `Lobophyllia hemprichii` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Porites cylindrica` <int> 61, 24, 0, 20, 0, 0, 0, 0, 0, 0, 1… ## $ `Porites lichen` <int> 0, 47, 49, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ `Porites lobata` <int> 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Porites lutea` <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Porites nigrescens` <int> 0, 0, 0, 21, 0, 9, 25, 0, 45, 26, … ## $ `Porites solida` <int> 0, 0, 10, 0, 17, 0, 31, 41, 0, 0, … ## $ `Porites stephensoni` <int> 0, 0, 0, 0, 0, 0, 0, 30, 0, 0, 0, … ## $ `Goniopora lobata` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Favia pallida` <int> 10, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ `Favia speciosa` <int> 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Favia stelligera` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Favia rotumana` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Favites abdita` <int> 33, 41, 23, 27, 91, 63, 72, 48, 71… ## $ `Favites chinensis` <int> 0, 44, 78, 61, 44, 0, 55, 30, 30, … ## $ `Goniastrea rectiformis` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0… ## $ `Goniastrea pectinata` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Goniastrea sp` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Dulophyllia crispa` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9… ## $ `Platygyra daedalea` <int> 0, 27, 55, 0, 71, 74, 55, 48, 0, 0… ## $ `Platygyra sinensis` <int> 47, 27, 56, 26, 0, 0, 0, 0, 0, 0, … ## $ `Hydnopora rigida` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Leptastrea purpurea` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Leptastrea pruinosa` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `Cyphastrea serailia` <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, … ## $ `Millepora platyphylla` <int> 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Millepora dichotoma` <int> 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Millepora intrincata` <int> 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ `Heliopora coerulea` <int> 461, 271, 221, 154, 0, 0, 0, 0, 0,… ## $ time <fct> 81, 81, 81, 81, 81, 81, 81, 81, 81… ## $ rep <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, …
Tikus Island coral data
Explore/Process data
- Convert abundance to cover
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Tikus Island coral data
Explore/Process data
- Convert abundance to cover (abundance is the length in cm of a 10m transect containing the species)
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Step 1. fix typo (rename) - backticks
tikus %>% rename(`Acropora aspera`=`Acropera aspera`)
Tikus Island coral data
Explore/Process data
- Convert abundance to cover
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Step 2. melt data (gather)
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep)
Tikus Island coral data
Explore/Process data
- Convert abundance to cover
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Step 3. Calculate Cover (mutate) (Abundance/10)
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10)
Tikus Island coral data
Explore/Process data
- Convert abundance to cover
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Step 4. Split species into Genera and Species (separate)
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10) %>%
separate(Species,c('Genera','Species'))
Tikus Island coral data
Explore/Process data
- Convert abundance to cover
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Step 5. Subset just ‘Acropora’ (filter)
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10) %>%
separate(Species,c('Genera','Species')) %>%
filter(Genera=='Acropora')
Tikus Island coral data
Explore/Process data
- Convert abundance to cover
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Step 6. Sum over all Species (group_by and summarise)
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10) %>%
separate(Species,c('Genera','Species')) %>%
filter(Genera=='Acropora') %>%
group_by(time,rep) %>%
summarise(SumCover=sum(Cover))
Tikus Island coral data
Explore/Process data
- Convert abundance to cover
- Mean cover of total Acropora per year
- NOTE there is a typo ‘Acropera’
Step 7. Summarise per year
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10) %>%
separate(Species,c('Genera','Species')) %>%
filter(Genera=='Acropora') %>%
group_by(time,rep) %>%
summarise(SumCover=sum(Cover)) %>%
group_by(time) %>%
summarise(Mean=mean(SumCover),
Var=var(SumCover))
## # A tibble: 6 x 3 ## time Mean Var ## <fct> <dbl> <dbl> ## 1 81 25.6 383. ## 2 83 0 0 ## 3 84 0 0 ## 4 85 2.43 14.2 ## 5 87 8.01 68.5 ## 6 88 8.55 106.
Tikus Island coral data
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10) %>%
separate(Species,c('Genera','Species')) %>%
filter(Genera=='Acropora') %>%
group_by(time,rep) %>%
summarise(SumCover=sum(Cover)) %>%
group_by(time) %>%
summarise(Mean=mean(SumCover),
Var=var(SumCover))
## # A tibble: 6 x 3 ## time Mean Var ## <fct> <dbl> <dbl> ## 1 81 25.6 383. ## 2 83 0 0 ## 3 84 0 0 ## 4 85 2.43 14.2 ## 5 87 8.01 68.5 ## 6 88 8.55 106.
Tikus Island coral data
Can you modify so that we get the means and var for each Genera per year?
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10) %>%
separate(Species,c('Genera','Species')) %>%
group_by(time,rep,Genera) %>%
summarise(SumCover=sum(Cover)) %>%
group_by(time,Genera) %>%
summarise(Mean=mean(SumCover),
Var=var(SumCover))
## # A tibble: 144 x 4 ## # Groups: time [6] ## time Genera Mean Var ## <fct> <chr> <dbl> <dbl> ## 1 81 Acropora 25.6 383. ## 2 81 Coeloseris 0.88 1.02 ## 3 81 Cyphastrea 0 0 ## 4 81 Dulophyllia 0 0 ## 5 81 Favia 0.6 1.16 ## 6 81 Favites 8.22 14.9 ## 7 81 Fungia 0.68 1.38 ## 8 81 Galaxea 1.46 3.23 ## 9 81 Goniastrea 0 0 ## 10 81 Goniopora 0 0 ## # … with 134 more rows
Tikus Island coral data
What about the means and var for the top 3 Genera per year (sorted from highest to lowest)?
tikus %>% rename(`Acropora aspera`=`Acropera aspera`) %>%
gather(Species, Abundance,-time,-rep) %>%
mutate(Cover=Abundance/10) %>%
separate(Species,c('Genera','Species')) %>%
group_by(time,rep,Genera) %>%
summarise(SumCover=sum(Cover)) %>%
group_by(time,Genera) %>%
summarise(Mean=mean(SumCover),
Var=var(SumCover)) %>%
top_n(3,Mean) %>%
arrange(desc(Mean))
## # A tibble: 18 x 4 ## # Groups: time [6] ## time Genera Mean Var ## <fct> <chr> <dbl> <dbl> ## 1 87 Montipora 27.4 966. ## 2 81 Acropora 25.6 383. ## 3 85 Montipora 20.5 171. ## 4 85 Porites 19.0 51.3 ## 5 88 Montipora 11.8 644. ## 6 81 Montipora 11.4 95.7 ## 7 81 Heliopora 11.1 262. ## 8 84 Montipora 11.0 70.5 ## 9 88 Porites 9.84 41.4 ## 10 88 Acropora 8.55 106. ## 11 87 Acropora 8.01 68.5 ## 12 87 Porites 4.49 35.8 ## 13 84 Porites 2.94 6.65 ## 14 85 Platygyra 2.55 8.74 ## 15 83 Porites 1.74 2.07 ## 16 84 Pavona 1.2 3.33 ## 17 83 Fungia 1.14 3.64 ## 18 83 Montipora 0.93 1.57